# Kwan Yan, Jialun Zhong and Yanzeng Li propose LEAP for efficient LLM scheduling [Canonical reading](https://escapemeridian.com/readings/kwan-yan-jialun-zhong-and-yanzeng-li-propose-leap-for-efficient-2fe47b04) In a paper submitted to arXiv on 30 October 2026, Kwan Yan, Jialun Zhong and Yanzeng Li propose LEAP, a system for predicting output length so a server can schedule requests predicted to be shorter first. The authors argue that existing predictors can lose ranking quality when the domain or task changes, and that retraining and prediction overhead can erode scheduling gains. LEAP uses the serving model’s prefill hidden states—the intermediate representations produced while processing an input—and a linear readout to estimate output length. It adapts that readout as requests finish, using their observed lengths as feedback without gradient computation or replaying historical data. The authors say CPU-based prediction and updates run asynchronously, overlapping GPU prefill. In the evaluated cross-domain transfers, the authors report that online adaptation improved Spearman correlation by 64.7% under a larger domain shift. In serving experiments, they report 15.9–40.8% lower average per-token latency than the evaluated length predictors, and 1.9% higher throughput than the strongest evaluated baseline with 30,569× fewer predictor parameters. These are reported experimental results, not evidence here of performance across other workloads or production settings. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Oct 30, 2026 (2026-10-30). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T12:09:33.991Z. First observed: 2026-11-02T05:04:44.763Z. Retrieved: 2026-11-02T09:49:18.812Z. - [LEAP: Efficient LLM Inference Scheduling with Generalizable Length Prediction](https://arxiv.org/abs/2610.41359) ## Limits The supplied evidence is the arXiv abstract, not the paper’s full evaluation details; the workloads, baselines and serving conditions behind the reported results are not specified here. The abstract reports results from evaluated transfers and serving experiments; it does not establish performance across other workloads or production settings. ## Open question How well would LEAP’s reported gains hold across a wider range of workloads and serving environments? ## Source-stated The authors propose LEAP to improve length-aware scheduling through generalizable length prediction, online adaptation and asynchronous CPU execution. > To improve the generalization and efficiency of length-aware scheduling, we propose \\textbf{LEAP}, a length prediction system with fast online adaptation and asynchronous CPU execution. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source-stated LEAP uses the serving model’s prefill hidden states and a linear readout to predict output length. > LEAP reuses the serving model's prefill hidden states, which contain information about the output length, and extracts this signal through a linear readout. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source-stated LEAP adapts its readout at test time using completed requests as supervision, without gradient computation or historical-data replay. > it updates the readout at test time through recursive least squares, using completed requests as supervision without gradient computation or historical-data replay. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source-stated The authors report that online adaptation improves Spearman correlation by 64.7% under a larger domain shift in the evaluated cross-domain transfers. > online adaptation improves Spearman correlation by 64.7\\% under a larger domain shift. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source-stated In serving experiments, the authors report 15.9–40.8% lower average per-token latency than evaluated length predictors, and 1.9% higher throughput with 30,569× fewer predictor parameters than the strongest evaluated baseline. > reduces average per-token latency by 15.9--40.8\\% compared with the evaluated length predictors, while achieving 1.9\\% higher throughput with $30{,}569\\times$ fewer predictor parameters than the strongest evaluated baseline. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source and contributor context Yan, Kwan, Zhong, Jialun, Li, Yanzeng · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## CPU The central processing unit: a general-purpose processor that carries out a computer's instructions. - [Definition reference](https://aws.amazon.com/what-is/cpu/) ## Graphics processing unit A chip that performs many calculations at once; the main hardware used to train and run AI models. - [Definition reference](https://en.wikipedia.org/wiki/Graphics_processing_unit) ## Distribution shift A change between the data a model was trained on and the data it meets in use, which can lower its accuracy. - [Definition reference](https://en.wikipedia.org/wiki/Domain_adaptation) ## Latency The delay between sending a request and receiving the result. - [Definition reference](https://developers.google.com/machine-learning/glossary#latency) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 7f7c9c9e-5bd7-4a31-827a-7e577d2e6fa2. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T12:09:33.991Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: c29df14ed5cdd67f00e77961a013fa14b0bfc4406c34f9382ad1d1d79e667b11. Source text hash: 8e4a241bf8a51096417de319cd8bf9d83390c735df7f75b7c2b8707d505e2609. ## Revision and source-check history ### Version 1 — 2026-11-10T12:09:33.991Z In a paper submitted to arXiv on 30 October 2026, Kwan Yan, Jialun Zhong and Yanzeng Li propose LEAP, a system for predicting output length so a server can schedule requests predicted to be shorter first. The authors argue that existing predictors can lose ranking quality when the domain or task changes, and that retraining and prediction overhead can erode scheduling gains. LEAP uses the serving model’s prefill hidden states—the intermediate representations produced while processing an input—and a linear readout to estimate output length. It adapts that readout as requests finish, using their observed lengths as feedback without gradient computation or replaying historical data. The authors say CPU-based prediction and updates run asynchronously, overlapping GPU prefill. In the evaluated cross-domain transfers, the authors report that online adaptation improved Spearman correlation by 64.7% under a larger domain shift. In serving experiments, they report 15.9–40.8% lower average per-token latency than the evaluated length predictors, and 1.9% higher throughput than the strongest evaluated baseline with 30,569× fewer predictor parameters. These are reported experimental results, not evidence here of performance across other workloads or production settings. ## Original source arXiv cs.AI · Article Original source date: Oct 30, 2026 (2026-10-30). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T12:09:33.991Z. First observed: 2026-11-02T05:04:44.763Z. Retrieved: 2026-11-02T09:49:18.812Z. - [LEAP: Efficient LLM Inference Scheduling with Generalizable Length Prediction](https://arxiv.org/abs/2610.41359) ## Limits The supplied evidence is the arXiv abstract, not the paper’s full evaluation details; the workloads, baselines and serving conditions behind the reported results are not specified here. The abstract reports results from evaluated transfers and serving experiments; it does not establish performance across other workloads or production settings. ## Open question How well would LEAP’s reported gains hold across a wider range of workloads and serving environments? ## Source-stated The authors propose LEAP to improve length-aware scheduling through generalizable length prediction, online adaptation and asynchronous CPU execution. > To improve the generalization and efficiency of length-aware scheduling, we propose \\textbf{LEAP}, a length prediction system with fast online adaptation and asynchronous CPU execution. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source-stated LEAP uses the serving model’s prefill hidden states and a linear readout to predict output length. > LEAP reuses the serving model's prefill hidden states, which contain information about the output length, and extracts this signal through a linear readout. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source-stated LEAP adapts its readout at test time using completed requests as supervision, without gradient computation or historical-data replay. > it updates the readout at test time through recursive least squares, using completed requests as supervision without gradient computation or historical-data replay. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source-stated The authors report that online adaptation improves Spearman correlation by 64.7% under a larger domain shift in the evaluated cross-domain transfers. > online adaptation improves Spearman correlation by 64.7\\% under a larger domain shift. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source-stated In serving experiments, the authors report 15.9–40.8% lower average per-token latency than evaluated length predictors, and 1.9% higher throughput with 30,569× fewer predictor parameters than the strongest evaluated baseline. > reduces average per-token latency by 15.9--40.8\\% compared with the evaluated length predictors, while achieving 1.9\\% higher throughput with $30{,}569\\times$ fewer predictor parameters than the strongest evaluated baseline. - [Supporting source](https://arxiv.org/abs/2610.41359) ## Source and contributor context Yan, Kwan, Zhong, Jialun, Li, Yanzeng · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## CPU The central processing unit: a general-purpose processor that carries out a computer's instructions. - [Definition reference](https://aws.amazon.com/what-is/cpu/) ## Graphics processing unit A chip that performs many calculations at once; the main hardware used to train and run AI models. - [Definition reference](https://en.wikipedia.org/wiki/Graphics_processing_unit) ## Distribution shift A change between the data a model was trained on and the data it meets in use, which can lower its accuracy. - [Definition reference](https://en.wikipedia.org/wiki/Domain_adaptation) ## Latency The delay between sending a request and receiving the result. - [Definition reference](https://developers.google.com/machine-learning/glossary#latency) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 7f7c9c9e-5bd7-4a31-827a-7e577d2e6fa2. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T12:09:33.991Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: c29df14ed5cdd67f00e77961a013fa14b0bfc4406c34f9382ad1d1d79e667b11. Source text hash: 8e4a241bf8a51096417de319cd8bf9d83390c735df7f75b7c2b8707d505e2609. ### uncertain — 2026-11-10T12:09:33.991Z — anthropic/claude-opus-5.5 Publication: 7f7c9c9e-5bd7-4a31-827a-7e577d2e6fa2 attribution — pass: The draft correctly attributes LEAP and its reported results to authors Kwan Yan, Jialun Zhong and Yanzeng Li, and frames the performance figures as the authors' reports rather than established facts. > Authors:Kwan Yan, Jialun Zhong, Yanzeng Li summary — uncertain: The reading is mostly faithful but has three readability gaps. (1) It never says why shorter-first matters. The source's premise is that this ordering reduces request latency; add that clause to the first sentence. (2) Spearman correlation is central to a headline result but unexplained; add a term tooltip saying it measures how well predicted lengths rank requests. (3) LLM and recursive least squares lack tooltips. The draft also drops that existing predictors are large, which gives the 30,569x parameter figure its context. > Length-aware scheduling reduces request latency in LLM serving by prioritizing shorter requests. date — pass: originalPublished is 2026-10-30. This matches the arXiv v1 submission date, and the draft's statement that the paper was submitted on 30 October 2026 is correct. The 2 November feed date was correctly not used. > \[v1\] Fri, 30 Oct 2026 07:23:27 UTC (300 KB) support — uncertain: Most claims are directly supported by the abstract. However, the passage saying CPU-based prediction and updates run asynchronously while overlapping GPU prefill slightly overstates the source. The abstract says prediction and updates run asynchronously on CPU, but only prediction and initial batch ordering overlap the remaining GPU prefill. Smallest repair: say that prediction and initial batch ordering overlap the remaining GPU prefill. The limitations and the question are appropriately conditional. > Prediction and updates run asynchronously on CPU, while prediction and initial batch ordering overlap the remaining GPU prefill. media — pass: The selected media is the verified direct link to the original arXiv abstract page. It is the most direct way to access the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. 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